| # documentation: https://twenty.com/developers/section/self-hosting | |
| # slogan: Open-source CRM platform for modern teams with customizable workflows and integrations | |
| # tags: crm,productivity,sales,open-source,business | |
| # logo: svgs/twenty.svg | |
| # port: 3000 | |
| services: | |
| postgres: | |
| image: 'postgres:16-alpine' | |
| volumes: |
This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.
Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.
A complete, decrypted mirror of every encrypted file in the Forza Horizon 6 install.
Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Principle: Protect the main thread. Optimize for its context economy. Delegate to preserve reasoning quality, not to maximize parallelism.
Reading >~3 files or unfamiliar territory to ground a task? Delegate to an Explore/general-purpose subagent with a tight question. Its reads stay in throwaway context; only the synthesis returns. Direct reads in the main
| to check if the server works - https://webrtc.github.io/samples/src/content/peerconnection/trickle-ice | |
| stun: | |
| stun.l.google.com:19302, | |
| stun1.l.google.com:19302, | |
| stun2.l.google.com:19302, | |
| stun3.l.google.com:19302, | |
| stun4.l.google.com:19302, | |
| stun.ekiga.net, | |
| stun.ideasip.com, |
A practical guide to running multiple Qwen3 models through a single llama-server instance using model routing. Covers embedding, reranking, and chat/vision models.
Tested on Windows with RTX 3090 (24GB VRAM), llama-server build from llama.cpp master branch. Last updated: 2025-03-09.